def cli(recommendations, set_size, journey_id):
model_path, _, test_path = postgres.get_best_model_paths()
df_test = s3.read_parquet(test_path)
model = s3.read_pickle(model_path)
if not journey_id:
click.echo("No Journey id specified. Try for example {}".format(
df_test.sample(5).index.tolist()))
return
elif journey_id not in df_test.index.tolist():
click.echo("Journey ID {} not found. Try for example {}".format(
journey_id,
df_test.sample(5).index.tolist()))
return
observation = df_test.loc[journey_id, :].copy()
observation = observation.drop(["ttj_sub_12", "ttj"])
interv_cols = [col for col in observation.index if "i_" in col[:2]]
dem_cols = [col for col in observation.index if "d_" in col[:2]]
click.echo("Journey {} --- Demographics".format(journey_id))
click.echo("---------------")
output = observation[dem_cols]
click.echo(output[output == 1])
# NOTE: Un-normalize age here.
# Get maximum age from journey data instead
click.echo("Age: {}".format(round(output["d_age"] * 78)))
click.echo("---------------")
click.echo("Use Model: {}".format(model.__class__.__name__))
click.echo("---------------")
observation[interv_cols] = 0
base_probability = model.predict_proba(observation.to_numpy().reshape(
1, -1))
click.echo("Base employment probability {:.4f}".format(
base_probability[0][1]))
click.echo("---------------")
click.echo("Intervention Recommendations".format(journey_id))
click.echo("---------------")
df_recs = get_top_recommendations(model,
observation,
set_size=set_size,
n=recommendations)
click.echo(df_recs)
def run(self):
params = yaml.load(open("./conf/base/parameters.yml"),
Loader=yaml.FullLoader)["evaluation_params"]
model = s3.read_pickle(self.input()[0].path)
model_id, test_path, train_path = get_model_info_by_path(
self.input()[0].path)
df_train = s3.read_parquet(train_path)
df_test = s3.read_parquet(test_path)
rec_error = get_aggregate_recommendation_error(
df_train,
df_test,
model,
params["set_size"],
params["num_recs"],
params["percent_sample"],
)
write_recommendation_eval(get_db_engine(), rec_error, model_id, params)
self.task_complete = True
def run(self):
df_test = s3.read_parquet(self.input()[0][1].path)
y_test = df_test.loc[:, "ttj_sub_12"]
X_test = df_test.drop(["ttj", "ttj_sub_12"], axis="columns")
lg = s3.read_pickle(self.input()[1].path)
metrics = evaluate(lg, X_test, y_test)
model_info_to_db(
engine=get_db_engine(),
model=lg,
metrics=metrics,
features=X_test.columns.tolist(),
date=self.date,
model_path=self.input()[1].path,
train_data_path=self.input()[0][2].path,
test_data_path=self.input()[0][3].path,
)
# NOTE: Set task as completed manually. Use the build-in
# luigi.contrib.postgres.CopyToTable Task would the right.
self.task_complete = True